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Emotion detection can be used by online businesses to improve the quality of their customer support, to understand their consumers better, and to make their marketing messaging more effective.
To detect emotions in the text our algorithm uses several levels of classifiers and filters. Here is how it works: the text is first processed with a syntactic parser. From here, we need to filter all the context surrounding a word or a phrase to understand its meaning. That's semantic analysis. Then the system classifies conversations into happy, sad, angry, resentful, and other types of feelings.
Our algorithm is incredibly shrewd when it comes to sarcasm. Humans aren't very good at separating sarcasm from sincerity online. They can guess that a sentence is sarcastic with 62% of accuracy. Our algorithm reaches an overall accuracy of 71%.
Nudity detector is an algorithm that automates content moderation for images. It can detect unacceptable illustrations, and can recognise nudity or other sexual content on 3D computer graphics.
In order to keep their apps clean, online companies employ entire departments to moderate content posted by their users. Our algorithm works faster than humans and doesn't demand monthly paychecks.
However, just because the color of skin prevails in the picture, it doesn't mean that the image posted by the user contains inappropriate content. This might just be a naked...hand. Our algorithm uses additional filters that recognize the differences in skin tone and can identify an object in the picture.
Certain nude pictures that are acceptable in one country or culture may not be very suitable in other countries or cultures. Our nudity detector understands regional and cultural nudity norms. When moderating content the algorithm takes into account the target audience's culture, age, and gender.
The more images the algorithm processes, the better it gets.
This algorithm is an extended version of the text-based emotion detection system.
The algorithm uses voice tone analyzer to recognize emotions in speech. In other words, it can understand not only what was said, but also how it was said.
Using this tool, companies can evaluate the effectiveness of their call centers. They can analyse customer conversations and use the results of this analysis to improve sales and customer service.